Association between Regular Aspirin Use and Circulating Markers of Inflammation: A Study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial
Bibliographic record
Abstract
BACKGROUND: Regular aspirin use may decrease cancer risk by reducing chronic inflammation. However, associations between aspirin use and circulating markers of inflammation have not been well studied. METHODS: Serum levels of 78 inflammatory markers were measured in 1,819 55- to 74-year-old men and women in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Data were combined from three completed case-control studies and reweighted to the PLCO screening arm. Self-reported aspirin and ibuprofen use (number of tablets taken per day/week/month) over the previous 12 months was collected at baseline. Associations between (i) nonregular (<4 tablets/month), (ii) low (1-4 tablets/week), (iii) moderate (1 tablet/day), or (iv) high (2+ tablets/day) regular aspirin or ibuprofen use and marker levels were assessed with weighted logistic regression. RESULTS: Aspirin use was nominally associated with (Ptrend across categories ≤ 0.05) decreased levels of chemokine C-C motif ligand 15 [CCL15; OR, 0.5; 95% confidence intervals (CI), 0.3-0.8; moderate versus nonregular use]; soluble vascular endothelial growth factor receptor 2 (sVEGFR2; OR, 0.7; 95% CI, 0.4-1.0); soluble tumor necrosis factor receptor 1 (sTNFR1; OR, 0.6; 95% CI, 0.4-0.9) and increased levels of CCL13 (OR, 1.3; 95% CI, 0.8-2.1); CCL17 (OR, 1.1; 95% CI, 0.7-1.9) and interleukin 4 (IL4; OR, 1.6; 95% CI, 0.9-2.8). Trends were not statistically significant following correction for multiple comparisons. Likewise, no statistically significant associations were observed between ibuprofen use and marker levels. CONCLUSIONS: No significant associations were observed between regular aspirin use and the inflammatory markers assessed. IMPACT: Additional studies are needed to better understand the relationship between aspirin use, chronic inflammation, and cancer risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".